scholarly journals Assessment of the Association between the Three-dimensional Shape of the Corolla and Two-dimensional Shapes of Petals Using Fourier Descriptors and Principal Component Analysis in Eustoma grandiflorum

2011 ◽  
Vol 80 (2) ◽  
pp. 200-205 ◽  
Author(s):  
Kaeko Nii ◽  
Saneyuki Kawabata
Keyword(s):  
Principal Component Analysis ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Fourier Descriptors ◽  
Two Dimensional ◽  
Eustoma Grandiflorum ◽  
Dimensional Shape ◽  
Three Dimensional Shape

scholarly journals Principal Component Analysis of Munich Functional Developmental Diagnosis

2021 ◽  
Vol 13 (2) ◽  
pp. 227-233
Author(s):  
Grażyna Pazera ◽  
Marta Młodawska ◽  
Jakub Młodawski ◽  
Kamila Klimowska
Keyword(s):  
Principal Component Analysis ◽  
Social Skills ◽  
Motor Development ◽  
Three Dimensional ◽  
Principal Component ◽  
Component Analysis ◽  
Three Dimensions ◽  
Matrix Analysis ◽  
Psychomotor Development ◽  
First Year Of Life

Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life. The tool is based on standardized tables of physical development and is used to detect developmental deficits. It consists of eight axes on which the following skills are assessed: crawling, sitting, walking, grasping, perception, speaking, speech understanding, social skills. Methods: The study included 110 children in the first year of life examined with the MFDD by the same physician. The score obtained on a given axis was coded as a negative value (defined in months) below the child’s age-specific developmental level. Next, we examined the dimensionality of the scale and the intercorrelation of its axes using polychoric correlation and principal component analysis. Results: Correlation matrix analysis showed high correlation of MFDD axes 1–4, and MFDD 6–8. The PCA identified three principal components consisting of children’s development in the areas of large and small motor skills (axis 1–4), perception (axis 5), active speech, passive speech and social skills (axis 6–8). The three dimensions obtained together account for 80.27% of the total variance. Conclusions: MFDD is a three-dimensional scale that includes motor development, perception, and social skills and speech. There is potential space for reduction in the number of variables in the scale.

Optimal mean two-dimensional principal component analysis with F-norm minimization

2017 ◽  
Vol 68 ◽  
pp. 286-294 ◽  
Author(s):  
Qianqian Wang ◽  
Quanxue Gao ◽  
Xinbo Gao ◽  
Feiping Nie
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Norm Minimization
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